This repository contains graduate-level coursework projects focused on statistical analysis, machine learning, dimensionality reduction, clustering, forecasting, and scientific data analysis using Python and Jupyter Notebooks. The projects emphasize both theoretical understanding and practical implementation across real-world datasets in healthcare, bioinformatics, computer vision, and oceanographic forecasting.
Comprehensive exploratory data analysis, covariance/correlation analysis, scaling methods, and univariate/bivariate statistical analysis using the Palmer Penguins dataset.
Analysis of high-dimensional cancer gene expression data using feature selection methods, PCA, and t-SNE for visualization and biomarker discovery.
Regression and classification modeling for heart failure clinical data using Linear Regression, Ridge/Lasso Regression, Logistic Regression, PCA, and feature selection techniques.
Clustering grayscale facial-expression images using K-Means, Gaussian Mixture Models, and Agglomerative Clustering with dimensionality reduction and feature selection workflows.
Spatiotemporal oceanographic data analysis using EOF decomposition, ARMA/ARIMA/Kalman filtering, and reconstruction of future sea surface height fields.
- Python
- NumPy
- Pandas
- Matplotlib / Seaborn
- Scikit-learn
- SciPy
- Statsmodels
- Exploratory Data Analysis (EDA)
- Statistical Computing
- Feature Selection
- PCA and t-SNE
- Clustering Algorithms
- Regression & Classification
- Time-Series Forecasting
- EOF/SVD Analysis
- Scientific Visualization
- Biomedical & Environmental Data Analytics
Florida Atlantic University
College of Electrical Engineering and Computer Science
CAP 5768-042 - Intro to Data Science - Spring 2026
Professor: Dr. Ali Ibrahim
TA: Mohsen Ahmadi